code: def test_lr_monitor_custom_name(tmpdir):
      class TestModel(BoringModel):
          def configure_optimizers(self):
              optimizer, [scheduler] = super().configure_optimizers()
              lr_scheduler = {
                  "scheduler": scheduler,
                  "name": "my_logging_name",
              }
              return optimizer, [lr_scheduler]
  
      lr_monitor = LearningRateMonitor()
      trainer = Trainer(
          default_root_dir=tmpdir,
          max_epochs=2,
          limit_val_batches=0.1,
          limit_train_batches=0.5,
          callbacks=[lr_monitor],
          enable_progress_bar=False,
          enable_model_summary=False,
          logger=CSVLogger(tmpdir),
      )
      trainer.fit(TestModel())
      assert list(lr_monitor.lrs) == ["my_logging_name"]
  
imports: import pytest
  import torch
  from lightning.pytorch import Trainer
  from lightning.pytorch.callbacks import EarlyStopping, LearningRateMonitor
  from lightning.pytorch.callbacks.callback import Callback
  from lightning.pytorch.callbacks.finetuning import BackboneFinetuning
  from lightning.pytorch.demos.boring_classes import BoringModel
  from lightning.pytorch.loggers import CSVLogger
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from torch import optim
  from tests_pytorch.helpers.datamodules import ClassifDataModule
  from tests_pytorch.helpers.runif import RunIf
  from tests_pytorch.helpers.simple_models import ClassificationModel
label_desc: Use image node_id 11 for calling a global function with example usage: test_lr_monitor_custom_name(tmpdir) and returns: optimizer,  list